Databricks for Beginners: Data Analytics – Pro Track

Practical Databricks+Delta Lake hands‑on course: ETL with PySpark,medallion pipelines,visualization,streaming & basic ML

What you’ll learn
✓ Build end-to-end Delta Lake pipelines (Bronze → Silver → Gold) on Databricks and persist managed Delta tables in Unity Catalog.
✓ Implement robust PySpark ETL: safe type casting, duplicate handling, schema enforcement, and scalable transformations.
✓ Optimize Spark jobs using partitioning, caching, join strategies and Spark UI diagnostics to reduce runtime and cost.
✓ Create production‑ready analytics: advanced SQL (CTEs, window functions, MERGE) and reusable business views.
✓ Produce clear visualizations and reports from Spark data using Databricks built‑in tools and Python libraries (Matplotlib/Seaborn).
✓ Build basic Structured Streaming pipelines with Delta Lake sinks and handle late/duplicate events with watermarking and deduplication.
✓ Apply an introductory ML workflow: feature preparation, model training/evaluation, MLflow tracking, and model persistence.

Requirements
● Basic programming familiarity (comfort with reading and editing Python code).
● Fundamental SQL knowledge (SELECT, JOIN, GROUP BY).
● A laptop/desktop with internet access.
● A Databricks account (Community Edition or trial) — instructions provided in Section 2
● Recommended but not required: basic pandas or Jupyter notebook experience for faster onboarding.
● No prior Spark or Databricks experience required — this course starts with workspace setup and guides you step‑by‑step.

Description
Practical Databricks + Delta Lake hands‑on course: ETL with PySpark, medallion pipelines, visualization, streaming & basic ML

This Pro Track course is designed to teach practical, job-ready Databricks skills specifically tailored for data engineers and analytics professionals. Beginning with the essentials of workspace setup and data ingestion, you will progressively build repeatable Bronze to Silver to Gold data pipelines using Delta Lake technology. Throughout the course, you will perform robust data cleaning and complex transformations using PySpark, and apply advanced SQL patterns to support comprehensive analytics. The curriculum places a strong emphasis on defensive engineering practices, including safe type casting, handling duplicates effectively, enforcing schemas, and implementing thorough testing strategies to ensure that your data pipelines run reliably and consistently in production environments.

In addition, you will learn how to design and execute transformation workflows efficiently, schedule Databricks Jobs for automation, and optimize Spark performance by leveraging techniques such as partitioning, caching, and analyzing the Spark UI. The course also includes modules on visualization and reporting, demonstrating how to convert cleaned Spark outputs into persuasive and insightful charts using Databricks’ built-in visualization tools as well as popular Python libraries like Matplotlib and Seaborn. Furthermore, the Pro Track covers fundamental concepts of structured streaming, including stateful operations, and introduces an introductory machine learning workflow utilizing MLlib and MLflow for experiment tracking and management.

Hands-on labs, real-world case studies based on datasets such as Netflix and IMDb, and downloadable notebooks provide ample opportunity to practice the entire end-to-end process. This includes ingesting raw data, cleaning and validating it, building Delta tables, creating data pipelines, visualizing insights, and deploying basic jobs. By the end of the course, you will be capable of implementing production-grade data pipelines, optimizing Spark jobs for performance, and presenting reliable, actionable analytics to stakeholders. Additionally, students who enroll during Early Access receive priority support through Q&A sessions and invitations to live interactive sessions for enhanced learning.

Who this course is for
■ Junior to mid‑level data engineers and analytics engineers who need to build reliable ETL pipelines and production Delta Lake workflows.
■ Data analysts and BI engineers who want to scale analyses from notebooks to repeatable pipelines and produce shareable visual reports.
■ Software engineers or backend engineers transitioning into data engineering who know basic Python/SQL and want hands‑on Spark/Databricks experience.
■ Team leads or technical contributors who must validate data quality, optimize Spark jobs, or implement simple streaming/ML workflows